The determinants of issuers’ long term credit ratings : American S&P500 index

  • Authors

    • ِِAfef Feki Krichene Ph. D in accounting and Finance in IHEC Sfax-Tunisia
    • Walid Khoufi Professor in Finance in IHEC Sfax-Tunisia
    2015-05-18
    https://doi.org/10.14419/ijaes.v3i1.4631
  • Credit Ratings, Determinants, Interest and Debt Coverage, Ordered Probit Model.

  • Abstract

    In this paper, we examine the impact that various financial and business profile variables have on credit ratings issued for the S&P500 firms by Moody’s. Our ordered probit model indicates that firms’ financial policy, size, liquidity, interest and debt coverage have the most pronounced effect on credit ratings. Our results show that different coefficients are associated to the increments of interest and debt coverage ratios. Business profile variables are not significant. Liquidity variable is also a significant determinant of the issuer long-term credit rating and not just the short term one.

  • References

    1. [1] Amato JD & Furfine CH (2004), Are credit ratings procyclical?, Journal of Banking & Finance 28, 2641-2677. http://dx.doi.org/10.1016/j.jbankfin.2004.06.005.

      [2] Altman EI & Katz S (1976), Statistical bond rating classification using financial and accounting data, In Schiff M & Sorter G, Proceedings of the Conference on Topical Research in Accounting (New York University Press), New York, 205-239.

      [3] Blume ME, Lim F & Mackinlay C (1998), the declining credit quality of U.S. corporate debt: Myth or reality? The Journal of Finance 53, 1389-1413. http://dx.doi.org/10.1111/0022-1082.00057.

      [4] Boot AWA, Milbourn TT & Schmeits A (2006), Credit ratings as coordination mechanisms. The Review of Financial Studies 19-1, 81-118.

      [5] Cantor R & Packer F (1994), the credit rating industry. Federal Reserve Bank of New York Quarterly Review 19-2, 1-26.

      [6] Ederington LH (1985), Classification models and bond ratings. The Financial Review 20, 237-262. http://dx.doi.org/10.1111/j.1540-6288.1985.tb00306.x.

      [7] Gray S, Mirkovic a & Ragunathan V (2006), the determinants of credit ratings: Australian evidence. Australian Journal of Management 31-2, 333-354. http://dx.doi.org/10.1177/031289620603100208.

      [8] Guttler a & Wahrenburg M (2007), the adjustment of credit ratings in advance of defaults. Journal of Banking and Finance 31, 751-767. http://dx.doi.org/10.1016/j.jbankfin.2006.05.014.

      [9] Horrigan JO (1966), the determination of long-term credit standing with financial ratios, Journal of Accounting Research 4, 44-62. http://dx.doi.org/10.2307/2490168.

      [10] Huang Z, Chen H, Hsu CJ, Chen WH & Wu S (2004), Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems 37, 543-558. http://dx.doi.org/10.1016/S0167-9236(03)00086-1.

      [11] Hwang RC, Chung H & Chu CK (2010), Predicting issuer credit ratings using a semi parametric method. Journal of Empirical Finance 17, 120-137. http://dx.doi.org/10.1016/j.jempfin.2009.07.007.

      [12] Kaplan RS & Urwitz G (1979), Statistical models of bond rat-ings: a methodological inquiry. Journal of Business 52, 231-261. http://dx.doi.org/10.1086/296045.

      [13] Kennedy D, Lakonishok J & Shaw W H (1992), Accommodat-ing Outliers and Nonlinearity in Decision Models; Professional Adaptation. Journal of Accounting, Auditing and Finance 7, 161-194.

      [14] Kim KJ & Ahn H (2012), A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Computers and Operations Research 39, 1800-1811. http://dx.doi.org/10.1016/j.cor.2011.06.023.

      [15] Lee YC (2007), Application of support vector machines to corporate credit rating prediction. Expert Systems with Applications 33, 67-74. http://dx.doi.org/10.1016/j.eswa.2006.04.018.

      [16] Moody's Investors Service (2010), Rating methodology Global Manufacturing Industry.

      [17] Moody's Special Comment (2006), Moody's Credit Rating Prediction Model.

      [18] Moody's Special Comment (2004), A User's Guide to Moody's Default Predictor Model: An accounting Ratio Approach.

      [19] Moody's (2002), Moody's Rating Methodology Handbook, Sovereign, Moody's Investor Service Inc.New York, 90-98.

      [20] Nenide B, Pricer RW & Camp SM (2010), The use of financial ratios for research: problems associated with and recommendations for using large databases.

      [21] [21] Partnoy F (2002), the paradox of credit ratings, The New York University Salomon Center Series on Financial Markets and Institutions, 9, 65-84. http://dx.doi.org/10.1007/978-1-4615-0999-8_4.

      [22] Pettit J, Fitt C, Orlov S & Kalsekar A (2004), The new world of credit ratings, UBS Investment Bank.

      [23] Pinches GE & Mingo KA (1973), a multivariate analysis of industrial bond ratings. Journal of Finance, 28, 1-18. http://dx.doi.org/10.1111/j.1540-6261.1973.tb01341.x.

      [24] Pogue TF & Soldofsky RM (1969), what’s in a bond rating? Journal of Financial and Quantitative Analysis, 4, 201-228. http://dx.doi.org/10.2307/2329840.

      [25] Poon WPH (2003), Are unsolicited credit ratings biased down-ward? Journal of Banking and Finance, 27, 593-614. http://dx.doi.org/10.1016/S0378-4266(01)00253-9.

      [26] Standard and Poor's (2001b), Corporate Ratings Criteria, McGraw-Hill, New York.

      [27] Standard and Poor's (2003), Corporate Ratings Criteria, Stand-ard and Poor's Corporation, New York.

      [28] Standard and Poor's (2008), Corporate Ratings Criteria.

      [29] Standard & Poor's (2011), Sovereign Government Rating Methodology and Assumptions, Standard & Poor's Ratings Direct on the Global Credit Portal, New York.

      [30] Tanthanongsakkun S & Treepongkaruna S (2008), Explaining credit ratings of Australian companies- An application of the Merton model. Australian Journal of Management, 33-2, 261-276. http://dx.doi.org/10.1177/031289620803300203.

      [31] West RR (1970), an alternative approach to predicting corpo-rate bond ratings. Journal of Accounting Research, 8, 118-125. http://dx.doi.org/10.2307/2674717.

      [32] Wijayatunga P, Mase S & Nakamura M (2006), Appraisal of companies with Bayesian networks, International Journal of Business, 1-3, 329-346. http://dx.doi.org/10.1504/ijbidm.2006.009138.

  • Downloads

    Additional Files

  • How to Cite

    Feki Krichene, ِِAfef, & Khoufi, W. (2015). The determinants of issuers’ long term credit ratings : American S&P500 index. International Journal of Accounting and Economics Studies, 3(1), 78-85. https://doi.org/10.14419/ijaes.v3i1.4631

    Received date: 2015-04-15

    Accepted date: 2015-05-11

    Published date: 2015-05-18